Gender Differences in Diabetes Rates Vary Sharply by Region

Values represent the percentage difference between men’s and women’s diabetes prevalence (age-standardized adults 18+).

30DayChartChallenge
Data Visualization
R Programming
2025
Analysis of WHO data reveals striking regional differences in diabetes prevalence between men and women. While European men have nearly 40% higher diabetes rates than women, African women historically had up to 20% higher rates than men. This visualization explores these gender disparities across continents from 1990-2022.
Published

April 24, 2025

Figure 1: Time series line chart (1990-2022) showing gender differences in diabetes prevalence across regions. Europe shows men have nearly 40% higher rates than women (positive values). Africa shows women historically had up to 20% higher rates than men (negative values), though this gap has narrowed. Asia shows minimal gender differences, while Oceania and Americas show moderate differences with women having slightly higher rates.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  countrycode,    # Convert Country Names and Country Codes
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
   camcorder      # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
diabetes_data_raw <- read_csv(here::here(
  'data/30DayChartChallenge/2025/WHO_prevalence_of_diabetes_3356.csv')) |> 
  clean_names()

3. Examine the Data

Show code
glimpse(diabetes_data_raw)
skim(diabetes_data_raw)

4. Tidy Data

Show code
### |- Tidy ----
diabetes_processed <- diabetes_data_raw |>  
  filter(
    indicator == "Prevalence of diabetes, age-standardized",
    dim2 == "18+  years"
  ) |>
  select(location, period, dim1, prevalence = fact_value_numeric) |>
  mutate(
    year = period,
    region = case_when(
      location %in% c("Global", "World") ~ "Global",
      TRUE ~ countrycode(location, "country.name", "continent")
    )
  )

gender_gap_data <- diabetes_processed |>
  filter(dim1 %in% c("Male", "Female")) |>
  pivot_wider(names_from = dim1, values_from = prevalence) |>
  filter(!is.na(Male) & !is.na(Female)) |>
  mutate(relative_difference = (Male - Female) / Female * 100) |>
  filter(!is.na(region)) |>
  group_by(region, year) |>
  summarize(
    avg_relative_difference = mean(relative_difference, na.rm = TRUE), 
    .groups = "drop"
    ) |>
  group_by(region) |>
  mutate(
    last_value = if_else(year == max(year), avg_relative_difference, NA_real_),
    highlight = case_when(
      region %in% c("Asia", "Europe", "Africa") ~ "highlight",
      TRUE ~ "base",
    )
  ) |>
  ungroup()

label_colors <- c("Europe" = "#D62828", "Asia" = "#0077B6", "Africa" = "#2A9D8F")

gender_gap_labels <- gender_gap_data |> 
  filter(!is.na(last_value), region %in% names(label_colors)) |>
  mutate(label_color = label_colors[region])

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c("Europe" = "#D62828", "Asia" = "#0077B6", "Africa" = "#2A9D8F")
  )

### |-  titles and caption ----
# text
title_text    <- str_wrap("Gender Differences in Diabetes Rates Vary Sharply by Region",
                          width = 60) 

subtitle_text <- str_glue("Values represent the **percentage difference** between **men's** and **women's** <br>
                          diabetes prevalence (age-standardized adults 18+).<br><br>
                          Positive values mean higher rates for men; negative means higher for women.")

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 24,
  source_text =  "World Health Organization (WHO) Global Health Observatory, 2024" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),
    
    # Grid elements
    panel.grid.minor = element_line(color = "gray65", linewidth = 0.05),
    panel.grid.major = element_line(color = "gray65", linewidth = 0.05),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot(gender_gap_data, aes(x = year, y = avg_relative_difference, group = region)) +
  
  # Geoms
  geom_line(
    data = gender_gap_data |> filter(region %in% c("Americas", "Oceania")),
    color = "gray80", linewidth = 1.2, lineend = "round"
  ) +
  geom_line(data = gender_gap_data |> filter(region == "Asia"), color = colors$palette[2], linewidth = 1.4) +
  geom_line(data = gender_gap_data |> filter(region == "Europe"), color = colors$palette[1], linewidth = 1.4) +
  geom_line(data = gender_gap_data |> filter(region == "Africa"), color = colors$palette[3], linewidth = 1.4) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
  geom_text_repel(
    data = gender_gap_labels,
    aes(label = region, color = label_color),
    xlim = c(2021, 2026),
    size = 3.8, fontface = "bold", hjust = 0,
    direction = "y", nudge_x = 1.5, segment.alpha = 0.4,
    box.padding = 0.4, show.legend = FALSE
  ) +
  # Add gray labels for context lines
  geom_text_repel(
    data = gender_gap_data |>
      filter(!is.na(last_value), region %in% c("Americas", "Oceania")),
    aes(label = region),
    color = "gray50",
    xlim = c(2021, 2026),
    size = 3.5, fontface = "plain", hjust = 0,
    direction = "y", nudge_x = 1.5, segment.alpha = 0.3,
    box.padding = 0.4, show.legend = FALSE
  ) +
  # Annotate
  annotate(
    "text", x = 1990, y = 6, label = "Men have higher prevalence ↑", 
     hjust = 0, size = 3, color = "gray30", fontface = "bold"
    ) +
  annotate(
    "text", x = 1990, y = -6, label = "Women have higher prevalence ↓", 
    hjust = 0, size = 3, color = "gray30", fontface = "bold"
    ) +
  annotate(
    "text", x = 2016, y = 42, 
    label = "In Europe, men have nearly\n40% higher diabetes rates", 
    size = 3, color = "#D62828", fontface = "bold", hjust = 0
    ) +
  annotate(
    "text", x = 1997, y = -21, 
    label = "In Africa, women historically had\nup to 20% higher diabetes rates", 
    size = 3, color = "#2A9D8F", fontface = "bold", hjust = 0
    ) +
  # Scales
  scale_x_continuous(
    breaks = seq(1990, 2020, by = 5),
    limits = c(1990, 2026),
    expand = expansion(mult = c(0, 0.05))
  ) +
  scale_y_continuous(
    breaks = seq(-20, 40, by = 10),
    limits = c(-22, 42),
    labels = function(x) paste0(ifelse(x > 0, "+", ""), x, "%")
  ) +
  scale_color_identity() +  
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Relative Difference in\nDiabetes Prevalence (Men vs Women)"
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.55),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.1,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 24, 
  width = 8, 
  height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1        camcorder_0.1.0   ggrepel_0.9.6     countrycode_1.6.0
 [5] scales_1.3.0      skimr_2.1.5       janitor_2.2.0     showtext_0.9-7   
 [9] showtextdb_3.0    sysfonts_0.8.9    ggtext_0.1.2      lubridate_1.9.3  
[13] forcats_1.0.0     stringr_1.5.1     dplyr_1.1.4       purrr_1.0.2      
[17] readr_2.1.5       tidyr_1.3.1       tibble_3.2.1      ggplot2_3.5.1    
[21] tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.4        
[37] magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4        withr_3.0.2      
[41] bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29    bit_4.5.0        
[45] ragg_1.3.3        hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[49] markdown_1.13     rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1    
[53] glue_1.8.0        xml2_1.3.6        renv_1.0.3        vroom_1.6.5      
[57] svglite_2.1.3     rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1         
[61] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_24.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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